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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JUITA : Jurnal Informatika bit-Tech JOURNAL EDUCATIONAL OF NURSING(JEN)
Muhammad Hudzaifah Nasrullah
Yarsi Pratama University
Computer Science & IT Nursing

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Vulnerability Assessment of Information Disclosure in Bimasoft CBT Muhammad Hudzaifah Nasrullah; Tilly Raycitra Widya; Lilik Tiara Giantri; Duta Arief Christanto; Dede Cahyadi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2838

Abstract

This research examines the security parameters of Bimasoft CBT, a prominent computer-based testing platform utilized extensively in Indonesia, particularly during the execution of UNBK and amid the Covid-19 pandemic. Although CBT systems present distinct advantages in terms of efficiency relative to traditional paper-based assessments, they concurrently introduce significant security concerns. This issue is particularly pertinent considering research indicating that students exhibiting high self-efficacy tend to be more inclined towards dishonest practices, potentially capitalizing on system vulnerabilities. The investigation concentrates on the “offline self-simulation” iteration of Bimasoft CBT, which permits autonomous hosting capabilities. The assessment methodology incorporated strategic planning, a technical examination of the system, identification of vulnerabilities utilizing tools such as Chrome DevTools and Burp Suite, and risk evaluation employing the CVSS 4.0 framework. The inquiry revealed two medium-risk vulnerabilities (CVSS score: 6.9) that jeopardize confidentiality, permitting students to access examination questions prior to login and secure tokens without the oversight of a supervisor. To address these concerns, three principal solutions are recommended: the implementation of back-end token validation, the restriction of access to examination questions via the WordPress REST API prior to login, and the avoidance of CSS for concealing critical content. These findings underscore the necessity of fortifying security within CBT systems to ensure equitable assessment, uphold academic integrity, and assist developers and policymakers in the advancement of digital examination platforms.
Performance Evaluation of Tuned and Untuned Machine Learning Models in Speech Emotion Recognition Muhammad Hudzaifah Nasrullah; Dede Cahyadi; Tilly Raycitra Widya; Ewin Suciana; Lilik Tiara Giantri
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.29015

Abstract

This analysis takes on a comparative review of three distinct machine learning approaches: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF) to ascertain emotional states in verbal communication by utilizing the RAVDESS resource. In this review, we perform a strategy that unites audio feature extraction, model training with or without tweaks to hyperparameters, and evaluation via metrics including accuracy, precision, recall, and F1-score. The assessment shows that, before any refinement, SVM secured the utmost accuracy of 79%, trailed by MLP at 76% and RF at 71%. Following optimization, only SVM exhibited an enhancement, reaching 80%, whereas MLP and RF displayed negligible or no improvement. An examination of the confusion matrix revealed that SVM produced the most uniformly distributed predictions and effectively reduced misclassification errors, particularly within the emotion categories of “calm” and “happy.” This investigation offers empirical substantiation of SVM as a robust baseline model for speech emotion recognition in localized settings, while simultaneously providing insights into model optimization and development that could inform future implementations in speech-based human–computer interaction.
Automatic diagnosis of rice plant diseases using VGG-16 and computer vision Al-Bahra Al-Bahra; Henderi Henderi; Nur Azizah; Muhammad Hudzaifah Nasrullah; Didik Setiyadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.26975

Abstract

Pathogens are organisms that cause disease in plants. In the case of rice, these pathogens can include fungi, bacteria, nematodes, protozoa, and viruses. This study aims to investigate rice plant diseases using a hybrid system that employs the visual geometry group-16 (VGG-16) architecture and computer vision techniques, alongside various optimization algorithms and hyperparameters. We utilize the convolutional neural network (CNN) architecture of VGG-16 for feature extraction, implementing a process known as transfer learning. Additionally, this research compares different optimization algorithms with the VGG-16 model to identify the most effective optimization for the CNN architecture applied to the tested dataset. The main contribution of this study is the development of a model for identifying rice plant diseases based on data collected using VGG-16 for feature extraction and neural networks for classification with specific parameters. Our findings indicate that the best optimization algorithm is stochastic gradient descent (SGD) with momentum, achieving training and validation loss results of 0.173 and 0.168, respectively. Furthermore, the training and validation accuracies were 0.95 and 0.957. The model’s performance metrics include an accuracy of 95.75, precision of 95.75, recall of 95.75, and an F1-score of 95.73.
Application of Artificial Intelligence in MRI Image Analysis for Radiological Diagnosis: A Systematic Review Muhammad Hudzaifah Nasrullah
JOURNAL EDUCATIONAL OF NURSING(JEN) Vol 8, No 1 (2025): Journal Educational of Nursing (JEN)
Publisher : STIKes RSPAD RSPAD Gatot Soebroto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37430/jen.v8i1.241

Abstract

Purpose: This systematic review critically evaluates recent advances in AI applied to MRI image analysis for radiological diagnosis, emphasizing improvements in diagnostic accuracy and clinical utility.Methodology: A systematic literature review (SLR) was conducted using PRISMA guidelines, employing a PICOC framework. A comprehensive search of the Scopus database was performed, and studies were selected based on strict inclusion/exclusion criteria through screening and synthesis.Findings: The review found that AI techniques significantly enhance MRI diagnostic performance (e.g., better tumor detection) and streamline workflows by automating routine tasks. It also notes growing publication trends from 2020–2024 in this field, reflecting increasing global research interest.Research Limitations: The review is limited by its reliance on a single database (Scopus) and a narrow publication window (2020–2024). Many included studies exhibit data biases and lack comprehensive external validation, which may affect generalizability.Practical Implications: These results suggest that AI integration can improve clinical workflows. The authors emphasize the need for standardized protocols and multidisciplinary collaboration to ensure safe and effective implementation of AI in radiological practice.Originality: This study provides an original contribution by systematically synthesizing the latest literature on AI applications in MRI diagnostics, offering a comprehensive overview of current methods and trends. It fills a gap by critically evaluating recent studies and outlining future research directions.
The Influence of Ultra-Processed Food on Childhood Obesity: A Systematic Review Evie Kusmiati; Erwin Santoso Sugandi; Syahroni Lubis; Intan Masita Hayati; Muhammad Hudzaifah Nasrullah
JOURNAL EDUCATIONAL OF NURSING(JEN) Vol 8, No 1 (2025): Journal Educational of Nursing (JEN)
Publisher : STIKes RSPAD RSPAD Gatot Soebroto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37430/jen.v8i1.243

Abstract

Purpose: This study aims to evaluate the impact of ultra-processed food (UPF) consumption on the risk of obesity in children through a systematic review of studies published between 2019 and 2024.Methodology: Using a Systematic Literature Review (SLR) method with the PRISMA approach, this study screened 319 articles from the Scopus database, ultimately selecting 13 relevant articles based on inclusion and exclusion criteria using the PICOC framework.Findings: A significant correlation exists between UPF consumption and childhood obesity risk. UPFs are associated with elevated BMI, increased waist circumference, nutrient deficiency, and addictive eating patterns. Socioeconomic status, educational setting, and advertising exposure exacerbate these adverse outcomes.Research Limitations: The primary constraints encompass methodological discrepancies across the analyzed studies, an absence of longitudinal data, and restricted applicability of findings to developing nations.Practical Implications: These findings endorse the development of evidence-based nutrition policies, including food labeling and UPF advertising restrictions for children.Originality: This research introduces a novel "3P" intervention framework (Product, Place, Promotion) for regulating UPF consumption, incorporating biological and social variables into a holistic analytical model.
Co-Authors Al- Bahra Dede Cahyadi Didik Setiyadi Duta Arief Christanto Erwin Santoso Sugandi Evie Kusmiati Ewin Suciana Henderi . Intan Masita Hayati Lilik Tiara Giantri Nur Azizah Syahroni Lubis Tilly Raycitra Widya